A microscopy image of a biological sample may include a variety of cell types and debris against a background. A researcher may want to obtain statistics of a particular type of cell that is present in the microscopy image. Such statistics may include a count of how may cells of the particular cell type are present in the image, the range of sizes of such cells, and the mean, median and mode of the sizes of such cells. Before any such statistics can be calculated, cells of the particular cell type in the microscopy image must be identified from the other cell types present in the microscopy image and also from any debris present in the microscopy image.
Further, to measure the size of a cell, the boundary of such cell may have to be identified. Manually identifying centers and boundaries of all cells of a particular type in an image is time consuming and may lead to fatigue and error on the part of the researcher. Further, the amount of time required to identify and measure cells in a plurality of microscopy images may not be feasible and therefore may limit the type of research that may be conducted.
Edge detection methods have been used to identify edges of objects in an image. However, such methods may not distinguish between objects that are images of cells of a particular cell type, and debris or cells of uninteresting cell types. In addition, such techniques may not be effective in identifying individual cells in images that include a confluent (i.e., adjacent or overlapping) population of cells. Such edge detection techniques may not be effective in identifying cell features, for example, if the contrast between such features and the background is not sufficiently large. Such lack of contrast may be an issue, for example, in images of cells that are not labeled with dyes or other markers. In addition, edge detection methods may not distinguish cells from other types of objects. Further, edge detection methods may not detect the entire edge enclosing an object and provide results that show sections of edges with gaps therebetween instead of an enclosed object.
Other techniques may evaluate an image to identify “bumps” of a particular size and/or shape and combine such bumps with edge information. However, such techniques do not identify irregular shapes well and combining bumps with edge infomration typically requires significant additional processing time. Graph cuts may be used to distinguish objects in an image from the background. However, this may be computationally intensive and not particularly well suited to processing of real-world images.
As a result, a need exists for an improved approach to identifying and measuring objects in a microscopy image.